The present embodiments relate to segmentation. In particular, nodules or other structures are identified from scan data, such as from computed tomography data.
Pulmonary nodule segmentation is one goal of computer-assisted diagnosis (CAD) for identifying lung tumors. For example, a CAD system identifies pulmonary nodules from chest computed tomography (CT) data. A semi-automatic robust segmentation solution may realize reliable volumetric measurement of nodules as part of lung cancer screening and management.
Intensity-based segmentation solutions, such as local density maximum algorithms, segment nodules in CAD systems. Although such solutions may perform satisfactorily for solitary nodules, these solutions may not separate nodules from juxtaposed surrounding structures, such as walls and vessels, due to similar intensities. Approaches that are more sophisticated have been proposed to incorporate nodule-specific geometrical constraints. However, juxtapleural, or wall-attached, nodules remain as a challenge because such nodules may not conform to standard geometrical assumptions. Another source of problem is rib bones which appear with high intensity values in CT data. Such high-intensity regions near a possible nodule may bias estimation of the nodule center.
Two approaches provide robust segmentation of juxtapleural cases. In a first approach, a global lung or rib segmentation is performed prior to the nodule segmentation. This global approach may be effective but also computationally complex and dependent on the accuracy of the whole-lung segmentation. In a second approach, a local non-target removal or avoidance is performed prior to the nodule segmentation. This local approach may be more efficient than the global approach but more difficult to achieve high performance due to the limited amount of information available for the non-target structures.